Identity verification using fingerprint images is a science that has developed steadily over many years. Traditionally, matching fingerprint images was a painstaking process of manually comparing a set of subject's fingerprint images ("search" prints) to a multitude of "file" fingerprint image sets. A typical file fingerprint set comprised a group of inked fingerprints, such as those made by law enforcement agencies after an arrest, or made during the process of application to certain civil service positions. A fingerprint expert with a magnifying device would spend hours pouring over hundreds of file fingerprint images in an attempt to match a search fingerprint image to an existing file image.
It could be easily appreciated that the manual fingerprint matching process was extremely time-consuming and error-prone. Furthermore, poorly obtained or incomplete search and file fingerprint images often made the process even more difficult. In order to address these problems, a number of manual fingerprint image matching techniques were developed. The most popular technique involved identifying certain distinctive characteristics, or minutiae, present in every fingerprint image. Common minutiae include such fingerprint characteristics as bifurcations and ridge endings. Minutiae were typically recorded with three coordinates: two coordinates "x" and "y" representative of the minutia's position relative to a coordinate system, and an angle "v" representing the average direction of lines in the vicinity of the minutia. Combinations of minutiae and their locations are unique to each individual. Thus, for easier future verification and classification, fingerprint images were represented by sets of minutiae and their coordinates. While this development improved the accuracy and speed of verification, manually matching minutiae was still a slow and arduous task.
Another, less popular fingerprint classification technique involved classifying fingerprints by pattern types. The various pattern types included arches, tented arches, loops and whorls. The patterns were matched by their number, relative location and orientation.
Soon after the computer revolution of the 1960's, numerous attempts to automate the fingerprint verification process were made. The approach of matching entire fingerprint images was quickly discarded as being too computation intensive. Instead, the technique of classifying fingerprint images by their minutiae was computerized, greatly improving the speed and accuracy over that of the manual process. For many years thereafter, developments in computer processing power and refinements to the minutiae identification algorithms further improved the speed and accuracy of the process.
Advancements in automated fingerprint minutiae identification made feasible the use of automatic fingerprint image verification systems for verifying identity of a subject in real time. Thus, a new security process of restricting access to secure areas to authorized personnel using fingerprint images was introduced. This was accomplished by enrolling authorized personnel into a fingerprint record database stored in a computer system by scanning in a selected fingerprint image of an authorized person and locating and recording coordinates of that fingerprint's minutiae in a minutia record stored in the computer system's memory. Typically, a fingerprint scanner was connected to an electronic locking mechanism of a door, such that a person desiring access to the area beyond had to place a designated finger on the scanning device so that a search fingerprint image could be obtained. The search fingerprint image was then compared to the multitude of file fingerprint images to determine whether the person was authorized to enter the area.
To speed up the identification process, in later systems authorized persons were given additional identifying elements, such as personal identification numbers (PINs) or magnetic strip cards embedded with identifying data. These elements were associated with the authorized person's minutia record. Typically these improved systems would work as follows: A person desiring access to an area first inputs an identifying element into an identification system, for example by entering a PIN code on a keypad, or by swiping a magnetic strip card. Then, the person places a designated finger on a scanning device so that a search fingerprint image is entered into the system. The system then retrieves the file fingerprint image associated with the identifying elements provided by the person and attempts to verify the person's identity by comparing the search and file fingerprint images with each other. If the minutiae coordinates and angles on the file and search images match, the person is allowed entry into the secured area; otherwise, entry is denied.
This approach is problematic for a number of reasons. Minutia are notoriously difficult to identify in a large number of cases. Furthermore, if even a small amount of minutia is missing from a search fingerprint image, perhaps due to dirt or a scratch on the fingerprint portion of the finger, the system would not provide positive identification. Even relatively slight angular motion of a person's finger during the process of scanning of the search fingerprint image may generate errors. Moreover, because minutiae are numerous and because several coordinates need to be stored for each minutia, a minutiae record consumes a significant amount of data storage space on a computer system in which it is stored.
Most importantly, the task of identifying and locating minutiae in a search fingerprint image and then matching the minutiae to a minutiae record of a file fingerprint image is extremely computation intensive and thus requires very significant computer processing power and resources. In an installation with multiple secure areas, even a high speed computer system would suffer a increasing slowdown if a number of identification processes were taking place simultaneously. Typically, even with powerful computer systems, the wait for identification can be as long as several seconds, and much longer in a large installation with many fingerprint identification security entry points. To compound the frustration of waiting for a positive identification, a person may be asked to repeat the procedure several times, if, for example, the person moved their finger during the acquisition of the search image.
A recent attempt to address the above problems was disclosed in U.S. Pat. No. 5,067,162 to Driscoll, Jr. et al. (hereinafter "Driscoll"). Driscoll teaches that instead of identifying minutiae on a fingerprint image, a plurality of small "reference sections" of a fingerprint, each containing a portion of the fingerprint image data, and their coordinate positions should be identified and stored in a reference record after the image is binarized (i.e., converted into a black and white digital format). Verification is performed by first scanning a search fingerprint image of a person claiming to be authorized, forming a verification image with a plurality of verify regions each corresponding to a position of one of the reference sections, determining a best match location, corresponding to a reference section at each verify region and then determining whether a match exists by determining the degree of similarity between: (1) image data of each best match location and the corresponding reference section, and (2) the relative positioning of the best match locations and the corresponding reference sections.
While the Driscoll approach solves some of the problems inherent in traditional minutiae identification, improving the accuracy and removing the requirement that the search fingerprint image be nearly perfect, it has a significant drawback. While the Driscoll system is less error prone than a typical minutia verification system, if a portion of a person's fingerprint which encompasses one or more reference sections is obscured by dirt or damaged by a scrape or the like, the verification accuracy drops significantly resulting in authorized persons being denied access because Driscoll requires that a substantial number of the individual reference sections are matched to particular verify regions on the search fingerprint image and because Driscoll relies on the relative positioning of the reference sections for match determination.
It would thus be desirable to provide a system and method for automatically verifying the identity of a subject using a fingerprint image with a high degree of speed and accuracy. It would further be desirable to ensure a high degree of accuracy even if the subject's fingerprint image to be verified is only partially visible due to damage or contamination.